Revolutionizing non-traumatic acute care: a review of the role of artificial intelligence and machine learning in triaging and diagnosis

Abstract

Acute care settings, including emergency medicine and intensive care units, comprise a substantial portion of healthcare and are essential in the prompt management of conditions that can prove fatal. Critical care conditions require timely management that can be delayed by high patient volumes and the need for complex clinical decision making. Artificial intelligence (AI) tools have been created to enhance diagnostic accuracy and optimize workflow to improve patient care. This narrative review discusses the current status of AI in acute care, with a focus on its applications in triaging and diagnosis. AI-enhanced electrocardiogram analysis, identification of myocardial infarction and acute coronary syndrome, and heart failure risk stratification led to better patient-specific management and improved results. AI models successfully determined and aided in the timely management of various acute conditions, including pneumonia, pulmonary embolism, and respiratory failure. The AI algorithms used accurately determined sepsis onset and course, superseding traditionally used clinical tools and leading to early diagnosis and reduced sepsis mortality. These models showed high sensitivity and specificity in diagnosing and triaging neurological conditions, including altered levels of consciousness, seizures, and intracranial hemorrhages. AI that involved advanced machine learning imaging software led to faster and more accurate stroke diagnosis. Diagnostic tools assisted by AI improved the detection and classification of acute pancreatitis, appendicitis, and gastrointestinal bleeding. AI has shown promising results in optimizing management in acute care settings. However, critical issues in data standardization, ethical considerations, and clinical workflow integration need to be addressed to enable clinical implementation.

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